Instructions to use Trelis/Llama-2-7b-chat-hf-function-calling-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Trelis/Llama-2-7b-chat-hf-function-calling-v3 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Trelis/Llama-2-7b-chat-hf-function-calling-v3", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Trelis/Llama-2-7b-chat-hf-function-calling-v3")
model = AutoModelForCausalLM.from_pretrained("Trelis/Llama-2-7b-chat-hf-function-calling-v3", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Trelis/Llama-2-7b-chat-hf-function-calling-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Trelis/Llama-2-7b-chat-hf-function-calling-v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Trelis/Llama-2-7b-chat-hf-function-calling-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Trelis/Llama-2-7b-chat-hf-function-calling-v3

SGLang

How to use Trelis/Llama-2-7b-chat-hf-function-calling-v3 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Trelis/Llama-2-7b-chat-hf-function-calling-v3" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Trelis/Llama-2-7b-chat-hf-function-calling-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Trelis/Llama-2-7b-chat-hf-function-calling-v3" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Trelis/Llama-2-7b-chat-hf-function-calling-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Trelis/Llama-2-7b-chat-hf-function-calling-v3 with Docker Model Runner:
```
docker model run hf.co/Trelis/Llama-2-7b-chat-hf-function-calling-v3
```

Questions answered randomly by the model

by Nuclear6 - opened Apr 17, 2024

Discussion

Nuclear6

Apr 17, 2024

If there is no corresponding function for the user's request, what will be the output here? How to limit its output to empty json? Have these issues been considered?

RonanMcGovern

Trelis org Apr 17, 2024

if there is no function called needed, then the model responds as normal.

Optionally, you can add a function to the metadata that returns an empty json (empty except for the function name) in circumstances you specify in the description

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